ckmeans: COP K-means algorithm

In conclust: Pairwise Constraints Clustering

Description

This function takes an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output.

Usage

ckmeans(data, k, mustLink, cantLink, maxIter = 100)

Arguments

data	The unlabeled dataset.
k	Number of clusters.
mustLink	A list of must-link constraints
cantLink	A list of cannot-link constraints
maxIter	Number of iteration

Details

This algorithm produces a clustering that satisfies all given constraints.

Value

A vector that represents the labels (clusters) of the data points

Note

The constraints should be consistent in order for the algorithm to work.

Author(s)

Tran Khanh Hiep Nguyen Minh Duc

References

Wagstaff, Cardie, Rogers, Schrodl (2001), Constrained K-means Clustering with Background Knowledge

See Also

Wagstaff, Cardie, Rogers, Schrodl (2001), Constrained K-means Clustering with Background Knowledge

Examples

data = matrix(c(0, 1, 1, 0, 0, 0, 1, 1), nrow = 4)
mustLink = matrix(c(1, 2), nrow = 1)
cantLink = matrix(c(1, 4), nrow = 1)
k = 2
pred = ckmeans(data, k, mustLink, cantLink)
pred

Example output

[1] 1 1 2 2

conclust documentation built on May 2, 2019, 1:07 p.m.